# ---------------------------------------
# 论文: Strip Pooling: Rethinking spatial pooling for scene parsing  (CVPR2020)
# Github地址: https://github.com/houqb/SPNet
# 微信公众号：AI缝合术
"""
2024年全网最全即插即用模块,全部免费!包含各种卷积变种、最新注意力机制、特征融合模块、上下采样模块，
适用于人工智能(AI)、深度学习、计算机视觉(CV)领域，适用于图像分类、目标检测、实例分割、语义分割、
单目标跟踪(SOT)、多目标跟踪(MOT)、红外与可见光图像融合跟踪(RGBT)、图像去噪、去雨、去雾、去模糊、超分等任务，
模块库持续更新中......
https://github.com/AIFengheshu/Plug-play-modules
"""
# ---------------------------------------

import torch
from torch import nn
import torch.nn.functional as F


class StripPooling(nn.Module):
    """
    Reference:
    """
    def __init__(self, in_channels, pool_size, norm_layer, up_kwargs):
        super(StripPooling, self).__init__()
        self.pool1 = nn.AdaptiveAvgPool2d(pool_size[0])
        self.pool2 = nn.AdaptiveAvgPool2d(pool_size[1])
        self.pool3 = nn.AdaptiveAvgPool2d((1, None))
        self.pool4 = nn.AdaptiveAvgPool2d((None, 1))

        inter_channels = int(in_channels/4)
        self.conv1_1 = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 1, bias=False),
                                norm_layer(inter_channels),
                                nn.ReLU(True))
        self.conv1_2 = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 1, bias=False),
                                norm_layer(inter_channels),
                                nn.ReLU(True))
        self.conv2_0 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, 1, 1, bias=False),
                                norm_layer(inter_channels))
        self.conv2_1 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, 1, 1, bias=False),
                                norm_layer(inter_channels))
        self.conv2_2 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, 1, 1, bias=False),
                                norm_layer(inter_channels))
        self.conv2_3 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, (1, 3), 1, (0, 1), bias=False),
                                norm_layer(inter_channels))
        self.conv2_4 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, (3, 1), 1, (1, 0), bias=False),
                                norm_layer(inter_channels))
        self.conv2_5 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, 1, 1, bias=False),
                                norm_layer(inter_channels),
                                nn.ReLU(True))
        self.conv2_6 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, 1, 1, bias=False),
                                norm_layer(inter_channels),
                                nn.ReLU(True))
        self.conv3 = nn.Sequential(nn.Conv2d(inter_channels*2, in_channels, 1, bias=False),
                                norm_layer(in_channels))
        # bilinear interpolate options
        self._up_kwargs = up_kwargs

    def forward(self, x):
        _, _, h, w = x.size()
        x1 = self.conv1_1(x)
        x2 = self.conv1_2(x)
        x2_1 = self.conv2_0(x1)
        x2_2 = F.interpolate(self.conv2_1(self.pool1(x1)), (h, w), **self._up_kwargs)
        x2_3 = F.interpolate(self.conv2_2(self.pool2(x1)), (h, w), **self._up_kwargs)
        x2_4 = F.interpolate(self.conv2_3(self.pool3(x2)), (h, w), **self._up_kwargs)
        x2_5 = F.interpolate(self.conv2_4(self.pool4(x2)), (h, w), **self._up_kwargs)
        x1 = self.conv2_5(F.relu_(x2_1 + x2_2 + x2_3))
        x2 = self.conv2_6(F.relu_(x2_5 + x2_4))
        out = self.conv3(torch.cat([x1, x2], dim=1))
        return F.relu_(x + out)


# 输入 B C H W,  输出 B C H W
if __name__ == '__main__':
    block = StripPooling(64, (20, 12), nn.BatchNorm2d, {'mode': 'bilinear', 'align_corners': True})
    input = torch.rand(3, 64, 32, 32)
    output = block(input)
    print(input.size(), output.size())
